peer reviewed | This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

International audience | This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices.

This article presents data designed by European researchers who performed a literature review and interpreted the results to determine impact factors of many agroecological practices on a wide variety of sustainability indicators. The impact factors are represented in a matrix that connects practices to indicators. The indicators are related to environmental, economic and social sustainability of a typical European integrated crop-livestock farm. The data are included in the serious game SEGAE to learn agroecology, as described in “SEGAE: a serious game to learn agroecology” [1]. The data can be modified to adapt the game to other agricultural systems. Finally, the data can be re-used in research projects as a basis to assess impacts of agroecological practices. | Sustainability indicators, crop-livestock integration, systems approach, transition management | 40 | 1-6 | 36

International audience | This article summarizes the data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland). Sampling sites were selected based on a double orthogonal gradient of size and connectivity and were urban green areas (e.g. parks, cemeteries). To characterize the flowering plants, two sampling methodologies were applied between April and July 2018. First, a floristic inventory of the occurrence of all flowering plants in the five cities. Second, flower counts in sampling plots of standardized size (1 m2) only in Zurich. We sampled 2146 plant species (contained in 824 genera and 137 families) and across the five cities. For each plant species, we provide its origin status (i.e. whether the plants are native from Europe or not) and 11 functional traits potentially important for plant-pollinator interactions. For each study site, we provide the number of species, genera, and families recorded, the Shannon diversity as well as the proportion of exotic species, herbs, shrubs and trees. In addition, we provide information on the patch size, connectivity, and urban intensity, using four remote sensing-based proxies measured at 100- and 800-m radii.

This article summarizes the data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland). Sampling sites were selected based on a double orthogonal gradient of size and connectivity and were urban green areas (e.g. parks, cemeteries). To characterize the flowering plants, two sampling methodologies were applied between April and July 2018. First, a floristic inventory of the occurrence of all flowering plants in the five cities. Second, flower counts in sampling plots of standardized size (1 m²) only in Zurich. We sampled 2146 plant species (contained in 824 genera and 137 families) and across the five cities. For each plant species, we provide its origin status (i.e. whether the plants are native from Europe or not) and 11 functional traits potentially important for plant-pollinator interactions. For each study site, we provide the number of species, genera, and families recorded, the Shannon diversity as well as the proportion of exotic species, herbs, shrubs and trees. In addition, we provide information on the patch size, connectivity, and urban intensity, using four remote sensing-based proxies measured at 100- and 800-m radii.

This article summarizes the data of a survey of flowering plants in 80 sites in five European cities and urban agglomerations (Antwerp, Belgium; greater Paris, France; Poznan, Poland; Tartu, Estonia; and Zurich, Switzerland). Sampling sites were selected based on a double orthogonal gradient of size and connectivity and were urban green areas (e.g. parks, cemeteries). To characterize the flowering plants, two sampling methodologies were applied between April and July 2018. First, a floristic inventory of the occurrence of all flowering plants in the five cities. Second, flower counts in sampling plots of standardized size (1 m2) only in Zurich. We sampled 2146 plant species (contained in 824 genera and 137 families) and across the five cities. For each plant species, we provide its origin status (i.e. whether the plants are native from Europe or not) and 11 functional traits potentially important for plant-pollinator interactions. For each study site, we provide the number of species, genera, and families recorded, the Shannon diversity as well as the proportion of exotic species, herbs, shrubs and trees. In addition, we provide information on the patch size, connectivity, and urban intensity, using four remote sensing-based proxies measured at 100- and 800-m radii. | info:eu-repo/semantics/publishedVersion

International audience | A long-term field experiment was conducted from 1989 to 2007 in northern France in a loamy soil to assess the cumulative effects of cropping systems (CSs) on soil compaction, soil porosity, soil structure, crop emergence and yield. Three CSs, including different crop rotations and cultivations (early or late sowing and harvesting), were compared. CS I was the succession of spring pea/winter wheat/oilseed rape (flax from 2001)/winter wheat while CSs II and III were the succession of sugar beet/winter wheat/maize/winter wheat. The latter two CSs consisted of different sowing dates, based on two distinct decision rules aimed at minimizing the risk of soil compaction in the CS II or maximizing the duration of the crop in the CS III. The tillage system was only mouldboard ploughing up to 20 0 0 while a new treatment with superficial tillage (i.e. at 6 cm depth) was integrated since then into the experiment to compare the effects of annual ploughing and reduced tillage on changes in soil structure over time. Soil water content was measured for each field operation by taking samples every 0.05 m up to a depth of 0.30 m in the topsoil. Soil compaction and soil structure was evaluated after each sowing using a morphological approach and soil bulk density measurements. The ‘‘profil cultural’’ method was used to map soil structure variations in the topsoil below the seedbed. Dry bulk density was measured with a gammaray transmission probe. Seedling emergence rates and crop yield were also measured in relation to CSs. This dataset represents an important description of the changes in the soil compaction level, crop emergence rates and yield, in relation to CSs and climate, and the overall impact on seedbed structure variations for major field crops under northern France conditions. This information can be used as input variables of several soil-crop models aiming at evaluating the impact of CSs and climate on soil compaction and seedbed structures.

A long-term field experiment was conducted from 1989 to 2007 in northern France in a loamy soil to assess the cumulative effects of cropping systems (CSs) on soil compaction, soil porosity, soil structure, crop emergence and yield. Three CSs, including different crop rotations and cultivations (early or late sowing and harvesting), were compared. CS I was the succession of spring pea/winter wheat/oilseed rape (flax from 2001)/winter wheat while CSs II and III were the succession of sugar beet/winter wheat/maize/winter wheat. The latter two CSs consisted of different sowing dates, based on two distinct decision rules aimed at minimizing the risk of soil compaction in the CS II or maximizing the duration of the crop in the CS III. The tillage system was only mouldboard ploughing up to 2000 while a new treatment with superficial tillage (i.e. at 6 cm depth) was integrated since then into the experiment to compare the effects of annual ploughing and reduced tillage on changes in soil structure over time. Soil water content was measured for each field operation by taking samples every 0.05 m up to a depth of 0.30 m in the topsoil. Soil compaction and soil structure was evaluated after each sowing using a morphological approach and soil bulk density measurements. The ‘‘profil cultural’’ method was used to map soil structure variations in the topsoil below the seedbed. Dry bulk density was measured with a gamma-ray transmission probe. Seedling emergence rates and crop yield were also measured in relation to CSs. This dataset represents an important description of the changes in the soil compaction level, crop emergence rates and yield, in relation to CSs and climate, and the overall impact on seedbed structure variations for major field crops under northern France conditions. This information can be used as input variables of several soil-crop models aiming at evaluating the impact of CSs and climate on soil compaction and seedbed structures.

International audience

Improved assessment and prediction of soil organic nitrogen (SON) mineralization is essential, as it contributes significantly to the nitrogen (N) nutrition of crops and remains a major economic and environmental challenge. SON mineralization is a function of soil properties, land use and climate, which led us to monitor a network of 137 cultivated fields covering the wide diversity of soils, crop rotations and cropping practices throughout Brittany (France). SON mineralization was quantified by the mineral N balance calculated for a maize crop not fertilized with N; it was determined by measuring soil mineral N (SMN) in the 0-90 cm soil profile in March (Ni) and October (Nf) and N uptake by the maize crop, and predicting nitrate leaching (Nₗₑₐcₕₑd) using the STICS model. SMN and plant N uptake were measured in triplicate. To predict Nₗₑₐcₕₑd, STICS was initialized at the date of Ni measurement. In addition, the experimental design was based on estimating SON for three consecutive years (2012-2014) to improve the accuracy of measuring mineralization. An indicator of the cropping system (I_Sys) was developed that integrated well the effects of crop rotation and the frequency of manure application; it can be considered a good index of effects of the cropping system on SON mineralization. This dataset may be used for a variety of applications, such as analysing effects of soil properties, cropping history and climatic conditions on SON mineralization, or evaluating the accuracy of soil-plant models (e.g. STICS, CERES).

International audience | The data presented here capture the structure of kitchen layouts belonging to consumers vulnerable to foodborne diseases and food risk-takers. Data were collected in the frame of the SafeConsume project by multidisciplinary research teams that visited consumers during preparing a meal and had the possibility to examine their cooking routines. Distances between sink and stove, sink and refrigerator, stove and refrigerator, sink and working place (countertop or table), stove and working place were analyzed to correlate food safety practices applied during cooking with kitchen arrangements. The results arising from analyzing the ergonomics of kitchens versus potential cross-contamination events are presented in Mihalache et al., [1]. These data contribute to a better understanding of real kitchen layouts and can be used as a starting point for future research regarding food safety-oriented arrangements instead of ergonomics-focused designs, for food safety risk assessments, as study cases for explaining specific measures that can be established to improve food handling and hygiene practices in homes and for sociological research pointing consumers' behavior during cooking.

The data presented here capture the structure of kitchen layouts belonging to consumers vulnerable to foodborne diseases and food risk-takers. Data were collected in the frame of the SafeConsume project by multidisciplinary research teams that visited consumers during preparing a meal and had the possibility to examine their cooking routines. Distances between sink and stove, sink and refrigerator, stove and refrigerator, sink and working place (countertop or table), stove and working place were analyzed to correlate food safety practices applied during cooking with kitchen arrangements. The results arising from analyzing the ergonomics of kitchens versus potential cross-contamination events are presented in Mihalache et al., [1]. These data contribute to a better understanding of real kitchen layouts and can be used as a starting point for future research regarding food safety-oriented arrangements instead of ergonomics-focused designs, for food safety risk assessments, as study cases for explaining specific measures that can be established to improve food handling and hygiene practices in homes and for sociological research pointing consumers’ behavior during cooking.

This data article provides spatially explicit data on greenhouse gas (GHG) emissions and mitigation potential at various administrative levels for the whole of Bangladesh. The results arising from analysis of this database are presented in research article “Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh” [1]. We collected crop and livestock management data and associated soil and climatic data from variety of primary and secondary sources outlined below in our methodology. The datafiles on crops and livestock contain model outputs for three greenhouse gases (CO2, CH4 and N2O) and their global warming potential, which are linked, to the information on crop/livestock management, soil and climatic conditions presented in the supplementary data of the associated manuscript. The datafiles on mitigation potential contain district-level annual GHG mitigation potential by 2030 and 2050 segregated by different crops/livestock types and mitigation options. This dataset is useful for Bangladesh's GHG accounting from the agricultural sector, and can be used to update its nationally determined contributions. Administrative level emissions and mitigation potential estimates segregated by crop-livestock types and mitigation options are useful to prioritize agricultural research and development interventions consistent with food security and environmental goals and to organize agricultural extension and support services to better inform farmers on food production and move towards GHG mitigation goals.

This data article provides spatially explicit data on greenhouse gas (GHG) emissions and mitigation potential at various administrative levels for the whole of Bangladesh. The results arising from analysis of this database are presented in research article “Quantifying opportunities for greenhouse gas emissions mitigation using big data from smallholder crop and livestock farmers across Bangladesh” [1]. We collected crop and livestock management data and associated soil and climatic data from variety of primary and secondary sources outlined below in our methodology. The datafiles on crops and livestock contain model outputs for three greenhouse gases (CO₂, CH₄ and N₂O) and their global warming potential, which are linked, to the information on crop/livestock management, soil and climatic conditions presented in the supplementary data of the associated manuscript. The datafiles on mitigation potential contain district-level annual GHG mitigation potential by 2030 and 2050 segregated by different crops/livestock types and mitigation options. This dataset is useful for Bangladesh's GHG accounting from the agricultural sector, and can be used to update its nationally determined contributions. Administrative level emissions and mitigation potential estimates segregated by crop-livestock types and mitigation options are useful to prioritize agricultural research and development interventions consistent with food security and environmental goals and to organize agricultural extension and support services to better inform farmers on food production and move towards GHG mitigation goals.

International audience | Legumes are important crop species as they produce highly nutritious seeds for human food and animal feed. In grain legumes, sub-optimal conditions affect seed developmental timing leading to impairment of seed quality traits acquired during seed maturation. To understand the molecular mechanisms of heat stress response in legume seeds, we analysed transcriptome changes of three seed tissues (i.e. em bryo, endosperm and seed coat) at four developmental stages, during seed maturation, from seed filling to mature dry seeds, collected under optimal and heat stress conditions in the model legume, Medicago truncatula (reference genotype A17). The total RNA sequencing generated a dataset of 48 samples, representing more than 57 Gb fastq raw data. Mapping, quantification and annotation of the data were based on fifth release of Medicago truncatula genome and provided expression profiles of 44,473 transcripts in seed tissues at different developmental stages and under optimal and stress conditions. Time-course and pairwise comparisons between optimal and stress conditions showed that 9182, 8315 and 3481 genes were differentially expressed due to heat stress in embryo, endosperm and seed coat respectively. Moreover, it highlighted a common set of 975 genes that were differentially expressed in all the seed tissues.

Legumes are important crop species as they produce highly nutritious seeds for human food and animal feed. In grain legumes, sub-optimal conditions affect seed developmental timing leading to impairment of seed quality traits acquired during seed maturation. To understand the molecular mechanisms of heat stress response in legume seeds, we analysed transcriptome changes of three seed tissues (i.e. em bryo, endosperm and seed coat) at four developmental stages, during seed maturation, from seed filling to mature dry seeds, collected under optimal and heat stress conditions in the model legume, Medicago truncatula (reference genotype A17). The total RNA sequencing generated a dataset of 48 samples, representing more than 57 Gb fastq raw data. Mapping, quantification and annotation of the data were based on fifth release of Medicago truncatula genome and provided expression profiles of 44,473 transcripts in seed tissues at different developmental stages and under optimal and stress conditions. Time-course and pairwise comparisons between optimal and stress conditions showed that 9182, 8315 and 3481 genes were differentially expressed due to heat stress in embryo, endosperm and seed coat respectively. Moreover, it highlighted a common set of 975 genes that were differentially expressed in all the seed tissues.

International audience | Diet and animal characteristics have a significant impact on the nitrogen (N)-use efficiency of dairy cows. A dataset (CowNflow) was built that compiles 28 N-balance experiments with Holstein dairy cows from 1983 to 2019, corresponding to 414 individual N flows, for a wide range of diets and animal characteristics. The dataset is composed of six Microsoft® Excel files that correspond to six levels of information. The main file, “CowNflow_6_Cow_measurements” reports individual weekly measurements of dry matter intake, daily faeces and urine excretion, milk production and composition, cow characteristics, and chemical composition of diets, faeces, urine, and milk. These raw data were used to calculate the N-balance, N-use efficiency, and nutrients’ in vivo total-tract digestibility. The experiments, conducted under standardised conditions, had multiple aims and offered a wide range of diets. Consequently, each diet is classified according to the main forage offered, resulting in six diet types: (1) maize forage (maize silage or dehydrated maize) alone, (2) maize forage and dehydrated lucerne, (3) maize forage and grass hay, (4) maize forage and freshly cut herbage, (5) freshly cut herbage alone, and (6) dehydrated herbage. The other five Excel files provide supplementary information at larger scales and describe experiment characteristics, experimental treatments, offered feeds along with their chemical composition, ingredient composition of compound feeds, and cow characteristics. This dataset can be used to better understand animal and dietary determinants of N-use efficiency and the origin of N losses to the environment, to identify feeding strategies that reduce protein-rich concentrate use, and to decrease environmental impacts of dairy farming with a variety of foraging systems.

Diet and animal characteristics have a significant impact on the nitrogen (N)-use efficiency of dairy cows. A dataset (CowNflow) was built that compiles 28 N-balance experiments with Holstein dairy cows from 1983 to 2019, corresponding to 414 individual N flows, for a wide range of diets and animal characteristics. The dataset is composed of six Microsoft® Excel files that correspond to six levels of information. The main file, “CowNflow_6_Cow_measurements” reports individual weekly measurements of dry matter intake, daily faeces and urine excretion, milk production and composition, cow characteristics, and chemical composition of diets, faeces, urine, and milk. These raw data were used to calculate the N-balance, N-use efficiency, and nutrients’ in vivo total-tract digestibility. The experiments, conducted under standardised conditions, had multiple aims and offered a wide range of diets. Consequently, each diet is classified according to the main forage offered, resulting in six diet types: (1) maize forage (maize silage or dehydrated maize) alone, (2) maize forage and dehydrated lucerne, (3) maize forage and grass hay, (4) maize forage and freshly cut herbage, (5) freshly cut herbage alone, and (6) dehydrated herbage. The other five Excel files provide supplementary information at larger scales and describe experiment characteristics, experimental treatments, offered feeds along with their chemical composition, ingredient composition of compound feeds, and cow characteristics. This dataset can be used to better understand animal and dietary determinants of N-use efficiency and the origin of N losses to the environment, to identify feeding strategies that reduce protein-rich concentrate use, and to decrease environmental impacts of dairy farming with a variety of foraging systems.

International audience | Environmental data on organic products are needed to assess their environmental performance. The purpose of the ACV Bio project reported here was to generate environmental data as life cycle assessment (LCA) data for a sample of French organic production systems including cropping systems (annual crops, intercrops, forages), grassland, wine grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs. LCA was used to estimate environmental impacts of products from these systems. Recommended uses are to characterize part of the diversity of French organic farming systems and some of their environmental impacts, identify areas for improvement, perform eco-design and sensitivity analysis, and/or make system choices in a given context. However, these data do not represent average French organic products and should not be used as such. The MEANS-InOut web application was used to generate life cycle inventories (LCI). Impact assessment was performed using SimaPro v9 software. The Environmental Footprint 2.0 characterisation method was used to generate LCA data. These data were supplemented with three LCA indicators: cumulative energy demand, land competition (CML-IA non-baseline) and biodiversity loss. Three non-LCA indicators were also calculated for certain systems: diversity of crop families (for cropping systems), agro-ecological in- frastructure (for sheep) and pesticide treatment frequency index (for grapes). In total, 173 products were modelled. LCA and non-LCA data are available in the Microsoft (R) Excel file at Data INRAE (https://doi.org/10.15454/TTR25S). LCI data are available in the AGRIBALYSE database and can be accessed using SimaPro and openLCA software. Farmer-practice data are available on demand.

Environmental data on organic products are needed to assess their environmental performance. The purpose of the ACV Bio project reported here was to generate environmental data as life cycle assessment (LCA) data for a sample of French organic production systems including cropping systems (annual crops, intercrops, forages), grassland, wine grapes, cow milk, calves, beef cattle, sheep, pigs, broilers and eggs. LCA was used to estimate environmental impacts of products from these systems. Recommended uses are to characterize part of the diversity of French organic farming systems and some of their environmental impacts, identify areas for improvement, perform eco-design and sensitivity analysis, and/or make system choices in a given context. However, these data do not represent average French organic products and should not be used as such. The MEANS-InOut web application was used to generate life cycle inventories (LCI). Impact assessment was performed using SimaPro v9 software. The Environmental Footprint 2.0 characterisation method was used to generate LCA data. These data were supplemented with three LCA indicators: cumulative energy demand, land competition (CML-IA non-baseline) and biodiversity loss. Three non-LCA indicators were also calculated for certain systems: diversity of crop families (for cropping systems), agro-ecological infrastructure (for sheep) and pesticide treatment frequency index (for grapes). In total, 173 products were modelled. LCA and non-LCA data are available in the Microsoft® Excel file at Data INRAE (https://doi.org/10.15454/TTR25S). LCI data are available in the AGRIBALYSE database and can be accessed using SimaPro and openLCA software. Farmer-practice data are available on demand.